KEGG: Kyoto Encyclopedia of Genes and Genomes. KEGG is a database resource for understanding high-level functions and utilities of the biological system, such as the cell, the organism and the ecosystem, from genomic and molecular-level information. It is a computer representation of the biological system, consisting of molecular building blocks of genes and proteins (genomic information) and chemical substances (chemical information) that are integrated with the knowledge on molecular wiring diagrams of interaction, reaction and relation networks (systems information). It also contains disease and drug information (health information) as perturbations to the biological system.

References in zbMATH (referenced in 260 articles )

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  1. Fernando Palluzzi, Mario Grassi: SEMgraph: An R Package for Causal Network Analysis of High-Throughput Data with Structural Equation Models (2021) arXiv
  2. He, Yinqiu; Xu, Gongjun; Wu, Chong; Pan, Wei: Asymptotically independent U-statistics in high-dimensional testing (2021)
  3. Zhu, Yuanyuan; Hu, Bin; Chen, Lei; Dai, Qi: iMPTCE-Hnetwork: a multilabel classifier for identifying metabolic pathway types of chemicals and enzymes with a heterogeneous network (2021)
  4. Granata, Ilaria; Guarracino, Mario Rosario; Maddalena, Lucia; Manipur, Ichcha: Network distances for weighted digraphs (2020)
  5. Li, Chunlin; Shen, Xiaotong; Pan, Wei: Likelihood ratio tests for a large directed acyclic graph (2020)
  6. Peeters, Carel F. W.; van de Wiel, Mark A.; van Wieringen, Wessel N.: The spectral condition number plot for regularization parameter evaluation (2020)
  7. Peng, Si; Shen, Xiaotong; Pan, Wei: Reconstruction of a directed acyclic graph with intervention (2020)
  8. Raymond Tobler, Angad Johar, Christian Huber, Yassine Souilmi: PolyLinkR: A linkage-sensitive gene set enrichment R package (2020) arXiv
  9. Su, Yansen; Zhu, Huole; Zhang, Lei; Zhang, Xingyi: Identifying disease modules based on connectivity and semantic similarities (2020)
  10. van Wieringen, Wessel N.; Stam, Koen A.; Peeters, Carel F. W.; van de Wiel, Mark A.: Updating of the Gaussian graphical model through targeted penalized estimation (2020)
  11. Wang, Yuhao; Segarra, Santiago; Uhler, Caroline: High-dimensional joint estimation of multiple directed Gaussian graphical models (2020)
  12. Wu, Chong; Xu, Gongjun; Shen, Xiaotong; Pan, Wei: A regularization-based adaptive test for high-dimensional GLMs (2020)
  13. Bucur, Ioan Gabriel; Claassen, Tom; Heskes, Tom: Large-scale local causal inference of gene regulatory relationships (2019)
  14. de Campos, Luis M.; Cano, Andrés; Castellano, Javier G.; Moral, Serafín: Combining gene expression data and prior knowledge for inferring gene regulatory networks via Bayesian networks using structural restrictions (2019)
  15. Gallicchio, Claudio; Micheli, Alessio: Deep reservoir neural networks for trees (2019)
  16. Jordi Martorell-Marugán, Víctor González-Rumayor, Pedro Carmona-Sáez: mCSEA: detecting subtle differentially methylated regions (2019) not zbMATH
  17. Kralj, Jan; Robnik-Sikonja, Marko; Lavrac, Nada: NetSDM: semantic data mining with network analysis (2019)
  18. Kuan-Hao Chao, Yi-Wen Hsiao, Yi-Fang Lee, Chien-Yueh Lee, Liang-Chuan Lai, Mong-Hsun Tsai, Tzu-Pin Lu, Eric Y. Chuang: RNASeqR: an R package for automated two-group RNA-Seq analysis workflow (2019) arXiv
  19. Nicholson, Daniel J.: Is the cell \textitreallya machine? (2019)
  20. Poterie, A.; Dupuy, J.-F.; Monbet, V.; Rouvière, L.: Classification tree algorithm for grouped variables (2019)

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